Data based truth maintenance

ABSTRACT

A truth maintenance method and system. The method includes receiving by a computer processor, health event data associated with heath care records for patients. The computer processor associates portions of the health event data with associated patients and related records in a truth maintenance system database. The computer processor derives first health related assumption data and retrieves previous health related assumption data derived from and associated with previous portions of previous health event data. The computer processor executes non monotonic logic with respect to the first health related assumption data and the previous health related assumption data. In response, the computer processor generates and stores updated first updated health related assumption data associated with the first health related assumption data and the previous health related assumption data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application claiming priority to Ser.No. 15/010,498 filed Jan. 29, 2016 which is a continuation applicationclaiming priority to Ser. No. 13/973,513 filed Aug. 22, 2013 now U.S.Pat. No. 9,280,743 issued Mar. 8, 2016 which is a continuationapplication claiming priority to Ser. No. 12/888,459 filed Sep. 23, 2010now U.S. Pat. No. 8,538,903 issued Sep. 17, 2013 and is related toApplication Ser. No. 12/888,476 filed on Sep. 23, 2010, the contents ofwhich are hereby incorporated by reference.

FIELD

The present invention relates to a method and associated system forgenerating health related assumptions based on events retrieved fromdata sources.

BACKGROUND

Generating predictions from specific data retrieved from various sourcestypically comprises an inefficient process with little flexibility.Predictions are typically generated without any regard to additionaldata. Predictions generated without any regard to additional data mayresult in inaccurate predictions.

SUMMARY

The present invention provides a method comprising: receiving, by acomputer processor of a computing device from a plurality of datasources, first health event data associated with a first plurality ofheath care records associated with a plurality of patients, the computerprocessor controlling a cloud hosted mediation system comprising aninference engine software application, a truth maintenance systemdatabase, and non monotonic logic; associating, by the computerprocessor, portions of the first health event data with associatedpatients of the plurality of patients; associating, by the computerprocessor, the portions of the first health event data with relatedrecords in the truth maintenance system database; deriving, by thecomputer processor executing the inference engine software application,first health related assumption data associated with each portion of theportions of the first health event data; retrieving, by the computerprocessor from the truth maintenance system database, previous healthrelated assumption data derived from and associated with previousportions of previous health event data retrieved from the plurality ofdata sources, the previous health related assumption data derived at atime differing from a time of the deriving, the previous health relatedevent data associated with previous health related events occurring at adifferent time from the first health event data; executing, by thecomputer processor, the non monotonic logic with respect to the firsthealth related assumption data and the previous health relatedassumption data; generating, by the computer processor executing the nonmonotonic logic and the inference engine software application, firstupdated health related assumption data associated with the first healthrelated assumption data and the previous health related assumption data;and storing, by the computer processor in the truth maintenance systemdatabase, the first health related assumption data and the first updatedhealth related assumption data.

The present invention provides a computer program product, comprising acomputer readable storage medium having a computer readable program codeembodied therein, the computer readable program code comprising analgorithm that when executed by a computer processor implements a methodwithin a computing device, the method comprising: receiving, by thecomputer processor from a plurality of data sources, first health eventdata associated with a first plurality of heath care records associatedwith a plurality of patients, the computer processor controlling a cloudhosted mediation system comprising an inference engine softwareapplication, a truth maintenance system database, and non monotoniclogic; associating, by the computer processor, portions of the firsthealth event data with associated patients of the plurality of patients;associating, by the computer processor, the portions of the first healthevent data with related records in the truth maintenance systemdatabase; deriving, by the computer processor executing the inferenceengine software application, first health related assumption dataassociated with each portion of the portions of the first health eventdata; retrieving, by the computer processor from the truth maintenancesystem database, previous health related assumption data derived fromand associated with previous portions of previous health event dataretrieved from the plurality of data sources, the previous healthrelated assumption data derived at a time differing from a time of thederiving, the previous health related event data associated withprevious health related events occurring at a different time from thefirst health event data; executing, by the computer processor, the nonmonotonic logic with respect to the first health related assumption dataand the previous health related assumption data; generating, by thecomputer processor executing the non monotonic logic and the inferenceengine software application, first updated health related assumptiondata associated with the first health related assumption data and theprevious health related assumption data; and storing, by the computerprocessor in the truth maintenance system database the first healthrelated assumption data and the first updated health related assumptiondata.

The present invention provides a computing system comprising a computerprocessor coupled to a computer-readable memory unit, the memory unitcomprising instructions that when executed by the computer processorimplements a method comprising: receiving, by the computer processorfrom a plurality of data sources, first health event data associatedwith a first plurality of heath care records associated with a pluralityof patients, the computer processor controlling a cloud hosted mediationsystem comprising an inference engine software application, a truthmaintenance system database, and non monotonic logic; associating, bythe computer processor, portions of the first health event data withassociated patients of the plurality of patients; associating, by thecomputer processor, the portions of the first health event data withrelated records in the truth maintenance system database; deriving, bythe computer processor executing the inference engine softwareapplication, first health related assumption data associated with eachportion of the portions of the first health event data; retrieving, bythe computer processor from the truth maintenance system database,previous health related assumption data derived from and associated withprevious portions of previous health event data retrieved from theplurality of data sources, the previous health related assumption dataderived at a time differing from a time of the deriving, the previoushealth related event data associated with previous health related eventsoccurring at a different time from the first health event data;executing, by the computer processor, the non monotonic logic withrespect to the first health related assumption data and the previoushealth related assumption data; generating, by the computer processorexecuting the non monotonic logic and the inference engine softwareapplication, first updated health related assumption data associatedwith the first health related assumption data and the previous healthrelated assumption data; and storing, by the computer processor in thetruth maintenance system database, the first health related assumptiondata and the first updated health related assumption data.

The present invention advantageously provides a simple method andassociated system capable of generating predictions from data retrievedfrom various sources.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for generating revisable assumptions basedon applying monotonic logic to healthcare data, in accordance withembodiments of the present invention.

FIG. 2 illustrates an algorithm used by the system of FIG. 1 forgenerating revisable assumptions based on applying monotonic logic tohealthcare data, in accordance with embodiments of the presentinvention.

FIG. 3 illustrates a computer apparatus used for generating revisableassumptions based on applying monotonic logic to healthcare data, inaccordance with embodiments of the present invention.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 5 for generating revisable assumptions basedon applying monotonic logic to healthcare related data, in accordancewith embodiments of the present invention. System 5 enables a method forproviding a truth maintenance system based on retrieving information viapatient specific data sources 7 a . . . 7 n and external medicalknowledge databases 12 a . . . 12 n. System 5 provides a smart cloudbased IT application for receiving, filtering, and analyzing patientdata from multiple sources received at different intervals stored in aclassification database and managed with a truth maintenance basedlogical algorithm. Dashboard views of the multiple assertions may begenerated based on new or updated information received.

System 5 of FIG. 1 comprises health care provider systems 14 a . . . 14n, patient specific data sources 7 a . . . 7 n (e.g., patient IDrecords, physician visit records, X-ray records, CT scan records,pharmacy records, lab test records, etc), and external medical knowledgedatabases 12 a . . . 12 n connected to a cloud hosted mediation system20 controlled by computing systems 4 a . . . 4 n. Although system 5 isdescribed with respect to retrieving information via patient specificdata sources 7 a . . . 7 n and external medical knowledge databases 12 a. . . 12 n, note that information may be retrieved via any type ofdatabase or receiver/transceivers (e.g., satellitereceiver/transceivers, any type of wireless receiver/transceivers, etc).Additionally, the information may be retrieved via a combination ofdifferent types of receiver/transceivers and/or databases. Cloud hostedmediation system 20 is controlled by multiple computers and networkdevices (i.e., computing systems 4 a . . . 4 n) all running in 100%virtualized mode with virtual machines running application software fordifferent functions. Cloud hosted mediation system 20 utilizes a cloudinfrastructure instantiating an application based on a consumption basedpay as you go delivery model. Patient specific data sources 7 a . . . 7n, external medical knowledge databases 12 a . . . 12 n, and health careprovider systems 14 a . . . 14 n are integrated into cloud hostedmediation system 20 using secure network protocols. Cloud hostedmediation system 20 comprises an Inference Engine (IE) 24 (softwareapplication) and advanced non monotonic logic 28 (i.e., executed as asoftware application) stored and managed in by a truth maintenancesystem (TMS) 22. TMS 22 comprises a data model. Classified event data(i.e., retrieved from patient specific data sources 7 a . . . 7 n andexternal medical knowledge databases 12 a . . . 12 n) is inputted intoinference engine 24 to derive plausible answers by applying rule basedreasoning to the classified event data. TMS 22 stores previouslyretrieved classified event data and applies new knowledge information tostored updated data (i.e., stored in classification database 26). I.E.24 derives plausible answers from retrieved evidence that iscontinuously being collected from multiple information sources (e.g.,patient specific data sources 7 a . . . 7 n, external medical knowledgedatabases 12 a . . . 12 n, etc). A subset of plausible answers for someforms of evidence may be stored in a database associated with IE 24.Additionally, new plausible answers are added to the database (i.e.,continuously) based on evidence collection. IE 24, TMS 22, and nonmonotonic logic 28 in combination provide classified medical/patientrelated data analysis from multiple patient specific data sources 7 a .. . 7 n and external medical knowledge databases 12 a . . . 12 n therebyimproving outcomes and accuracy before health care provider systems 14 a. . . 14 n retrieve the outcomes.

System 5 provides a cloud hosted smart healthcare system with IE 24 andan advanced non-monotonic logic processing system (i.e., and nonmonotonic logic 28) that is stored and managed along with TMS 22.Classified patient data from multiple sources (e.g., patient specificdata sources 7 a . . . 7 n) is processed by IE 24 in order to deriveplausible answers by applying rule based reasoning. TMS 22 retains pastinferred data and applies new knowledge information (e.g., new labreports, etc) to store updated data. IE 24, TMS 22, and non-monotoniclogic 28 combined together provide classified and analyzed diagnosticassertions thereby improving health outcomes and accuracy. System 5 ismaintained in a pay-by-usage cloud environment. Cloud hosted service isfurther integrated into external medical knowledge databases 12 a . . .12 n (e.g., DX plain, FDA knowledgebase, etc) to provide comprehensiveinformational dashboard views and reports (e.g., comprising informationsuch as medicines that address an issue) suitable for a patient profile.Physicians may use the reports for diagnostic decision making. System 5combines advanced artificial intelligence approaches (for patient datafrom multiple sources) integrated with a medical knowledgebase anddelivered in a cloud based usage model. System 5 provides a knowledgebased artificial intelligence system combined with a TMS based systemand non-monotonic logic reasoning for predicting health outcomes forpatients. Inferences are applied to patient information arriving atdifferent time intervals and TMS assertions are updated withplausibility values based on the new events.

Non monotonic logic 28 provides non monotonic reasoning with respect tosystem 5. Non monotonic reasoning comprises an approach in which axiomsand/or rules of inference are extended to make it possible to reasonwith incomplete information. Additionally, non monotonic reasoningallows for reasoning that allows system 5 to back track a reasoningsequence and make an alternate decision. The following implementationexample 1 describes non monotonic reasoning as follows:

EXAMPLE 1

In example 1 a person looks outside his/her house to see that it iscurrently not raining and that the sky is clear (i.e., evidence 1).Therefore the person determines that there is very little chance ofrain. The person decides to walk to work without taking an umbrella(i.e., action 1). After taking a few steps outside the house the personnotices that dark clouds are forming (i.e. evidence 2). Additionally,the person notices bolt of lightning in the distance (i.e., evidence 3)and determines that there is currently a very high likelihood of rain.Based on this new information, the person walks back to the house (i.e.,action 2) and picks up and opens an umbrella and then continues to walkto work. (i.e., action 3). The aforementioned example demonstrates thata person believes in one outcome (such as there is very little chance ofrain) based on evidence 1 and decides to take on a course of action(i.e., action 1) based on an initial belief. As new facts (i.e.,evidence 2 and 3) become known which appear to contradict the initialbelief the person develops a new belief (such as there is a very highlikelihood of rain) and based on the new updated belief, the personbacktracks on his/her initial decision and decides to take anotherdifferent set of actions (i.e., actions 2 and 3).

Non monotonic logic in combination with a Dempster Shafer (D-S) theoryis used to generate possible outcomes. In system 5, the utility ofprobability theory for modeling reasoning with uncertainty is limited bya lack of sufficient data to accurately estimate prior and conditionalprobabilities required in using Bayes' rule. D-S theory sidesteps therequirement for this data. D-S theory accepts an incompleteprobabilistic model without prior or conditional probabilities. Giventhe incompleteness of the model, D-S theory does not answer arbitraryprobabilistic questions. Rather than estimating the probability of ahypothesis, D-S theory uses belief intervals to estimate how closeevidence is to determining a truth of a hypothesis. A non monotonicapproach in accumulating evidence comprises provisions for retractingevidence and the D-S approach may be used together with a non monotonicapproach to determine how much belief should be assigned to each set ofevidence. System 5 computes a probability (i.e., a percentage) for eachassumption as new evidence is retrieved. System 5 enables a programmedimplementation (e.g., via a software application) the D-S theory ofMathematical evidence. The use of the D-S approach requires inferenceengine 24 to deduce belief functions. TMS 22 comprises a system/programthat provides a symbolic mechanism for identifying a set of assumptionsneeded to assemble desired proofs so that when probabilities of theassumptions are assigned. TMS 22 may be used as a symbolic engine forcomputing degrees of belief sought by the D-S theory. Additionally, TMS22 handles an effect of retracting assumptions that have beeninvalidated by evidence. TMS additionally keeps track of multipleplausible sets of assertions which may coexist in the absence ofcomplete knowledge. The following example 2 describes an implementationexample (i.e., with respect to example 1 comprising the rain/no rainexample) for implementing TMS 22.

EXAMPLE 2

In example 2, a belief there is “little chance of rain” (as inexample 1) is maintained in TMS 22 as one set of assumptions (i.e., set1). A belief that “there is a very high likelihood of rain” (as inexample 1) is maintained in TMS 22 as second set of assumptions (i.e.,set 2). Set 2 is favored with higher belief as compared to set 1. Astime passes, the person notices that the clouds start to fade away andit becomes very sunny outside (i.e., evidence 4). The person checks aweather forecast using a portable device weather application and findsthere is very little possibility of rain (i.e., evidence 5). The personfolds the umbrella and continues to walk to work. (i.e., action 4).Based on the aforementioned processing, two new evidences are generatedwhich result in supporting assumptions in set 1. Therefore, assumptionsset 1 are now more highly favored instead of assumptions in set 2.

The following implementation example 3 enabled by system 5 of FIG. 1comprises applying non monotonic reasoning in conjunction with TMS 22and the D-S theory of mathematical evidence in order to explore multiplepossible outcomes at a same time (or in parallel) while allowingback-tracking in real time thereby recommending different outcomes asnew evidence becomes known.

EXAMPLE 3

Example 3 comprises a patient medical diagnosis scenario. In a firststep, diagnosis related data associated with multiple patients isgenerated by different database feeders (e.g., doctor's offices,diagnostic centers, hospitals, etc). The diagnosis related data mayinclude patient specific details such as, inter alia, lab reports, CTscan reports, physician comments from patient visits, etc. The diagnosisrelated data is fed directly into system 5 which sorts the diagnosisrelated data based on patient ids and associates the diagnosis relateddata to an existing record (or adds it as a new record). System 5associates related events together and loads the associated events intoa classification DB (e.g., classification DB 26). IE 24 crawls throughclassified data (i.e., facts) stored in the classification DB andderives assumption type answers (e.g., set 1). Two pieces of evidence(i.e., of set 1) support the fact that a patient A has medical conditionof xx with a plausibility of 30% and 3 pieces of evidence generated(i.e., set 2) supports the fact that patient A has a medical conditionyy with a plausibility of 50%. Both sets of beliefs (i.e., set 1 and set2) are processed using the D-S theory of evidence and which illustratesthat one belief is higher than the other. The two sets (set 1 and set 2)of beliefs established are stored in TMS 22. Based on the aforementionedprocessing, system 5 generates an initial recommendation based on set 2and its associated higher belief assignment by the D-S theoryapplication. As time passes the diagnosis related data is updated withnew information as multiple new events and related evidence is generatedby the database feeders. This additional data is fed into system 5 whichlogs the events and associates them together inside the classificationDB. IE 24 crawls through the classified data (facts) stored in thedatabase and derives assumption type answers. For example, fivedifferent events and associated evidence support the fact that patient Ahas the medical condition yy with a plausibility of 30% and only 2pieces of evidence generated support the fact that patient A has themedical condition xx with a plausibility of 10%). The sets of beliefsestablished by the previous step and assigned using D-S theory are usedto update the belief in the set 1 and set 2 which are being stored inTMS 22. As a result of this update in the D-S theory belief functions,set 1 now states that patient A has medical condition yy with aprobability of 80% and set 2 now says that patient A has medicalcondition xx with probability of 10%. System 5 generates a newrecommendation based on set 1 and its associated higher beliefassignment by the D-S theory application. The new recommendation iscorrelated with a patients existing medical records and with an externalmedical knowledgebase for possible treatment options presented indashboard type views to a healthcare provider (i.e., this is example ofnon-monotonic reasoning where a decision has been reversed as newinformation is uncovered). IE 24, TMS 22, the D-S theory application,internal knowledgebase, integration to external knowledgebase, anddashboard user interface are all implemented on cloud based pay as yougo model transparent to an end user.

FIG. 2 illustrates an algorithm used by system 5 of FIG. 1 forgenerating revisable assumptions based on applying monotonic logic tohealthcare related data, in accordance with embodiments of the presentinvention. In step 200, a computer processor of a computing device(e.g., computing device 10 in FIG. 1 ) receives (from data sources suchas patient specific data sources 7 a . . . 7 n in FIG. 1 ) health eventdata associated with heath care records associated with patients. Thecomputer processor controls a cloud hosted mediation system (e.g., cloudhosted mediation system 20 of FIG. 1 ) comprising an inference enginesoftware application (e.g., inference engine software application 24 ofFIG. 1 ), a truth maintenance system database (e.g., truth maintenancesystem database 22 of FIG. 1 ), and non monotonic logic (e.g., nonmonotonic logic 28 of FIG. 1 ). In step 202, the computer processorassociates portions of the health event data with associated patients.In step 204, the computer processor associates the portions of thehealth event data with related medical records in a truth maintenancesystem database. In step 208, the computer processor (i.e., executingthe inference engine software application) derives health relatedassumption data associated with each portion of the health event data.In step 212, computer processor retrieves (i.e., from the truthmaintenance system database) previous health related assumption dataderived from and associated with previous portions of previous healthevent data retrieved from the data sources. The previous health relatedassumption data derived at a time differing from a time of deriving thehealth related assumption data in step 208. The previous health relatedevent data is associated with previous health related events occurringat a different time from the health events received in step 200. In step214, the computer processor executes the non monotonic logic withrespect to the health related assumption data and the previous healthrelated assumption data. The non monotonic logic may be executed as asoftware program. In step 218, the computer processor generating (i.e.,in response to executing the non monotonic logic and the inferenceengine software application) updated health related assumption dataassociated with the health related assumption data and the previoushealth related assumption data. In step 224, the computer processorstores (i.e., in the truth maintenance system database) the healthrelated assumption data and the updated health related assumption data.In step 228, the computer processor executes (i.e., based on the updatedhealth related assumption data) a health related action associated withthe patients. The actions may include, inter alia, implementing a pay byusage cloud metering model and presenting the actions to hospitals,physicians, insurance companies, etc.

FIG. 3 illustrates a computer apparatus 90 (e.g., computing systems 4 a. . . 4 n of FIG. 1 ) used for generating revisable heath relatedassumptions based on applying monotonic logic to healthcare relateddata, in accordance with embodiments of the present invention. Thecomputer system 90 comprises a processor 91, an input device 92 coupledto the processor 91, an output device 93 coupled to the processor 91,and memory devices 94 and 95 each coupled to the processor 91. The inputdevice 92 may be, inter alia, a keyboard, a software application, amouse, etc. The output device 93 may be, inter alia, a printer, aplotter, a computer screen, a magnetic tape, a removable hard disk, afloppy disk, a software application, etc. The memory devices 94 and 95may be, inter alia, a hard disk, a floppy disk, a magnetic tape, anoptical storage such as a compact disc (CD) or a digital video disc(DVD), a dynamic random access memory (DRAM), a read-only memory (ROM),etc. The memory device 95 includes a computer code 97. The computer code97 includes algorithms (e.g., the algorithm of FIG. 2 ) for generatingrevisable heath related assumptions based on applying monotonic logic tohealthcare related data. The processor 91 executes the computer code 97.The memory device 94 includes input data 96. The input data 96 includesinput required by the computer code 97. The output device 93 displaysoutput from the computer code 97. Either or both memory devices 94 and95 (or one or more additional memory devices not shown in FIG. 3 ) maycomprise the algorithm of FIG. 2 and may be used as a computer usablemedium (or a computer readable medium or a program storage device)having a computer readable program code embodied therein and/or havingother data stored therein, wherein the computer readable program codecomprises the computer code 97. Generally, a computer program product(or, alternatively, an article of manufacture) of the computer system 90may comprise the computer usable medium (or said program storagedevice).

Still yet, any of the components of the present invention could becreated, integrated, hosted, maintained, deployed, managed, serviced,etc. by a service provider who offers to generate revisable heathrelated assumptions based on applying monotonic logic to healthcarerelated data. Thus the present invention discloses a process fordeploying, creating, integrating, hosting, maintaining, and/orintegrating computing infrastructure, comprising integratingcomputer-readable code into the computer system 90, wherein the code incombination with the computer system 90 is capable of performing amethod for generating revisable heath related assumptions based onapplying monotonic logic to healthcare related data. In anotherembodiment, the invention provides a method that performs the processsteps of the invention on a subscription, advertising, and/or fee basis.That is, a service provider, such as a Solution Integrator, could offerto generate revisable heath related assumptions based on applyingmonotonic logic to healthcare related data. In this case, the serviceprovider can create, maintain, support, etc. a computer infrastructurethat performs the process steps of the invention for one or morecustomers. In return, the service provider can receive payment from thecustomer(s) under a subscription and/or fee agreement and/or the serviceprovider can receive payment from the sale of advertising content to oneor more third parties.

While FIG. 3 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 3 . For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention. All descriptions ofmethods and processes comprising steps herein are not limited to anyspecific order for performing the steps.

What is claimed is:
 1. A method comprising: receiving, by a computerprocessor of a computing device from a plurality of data sources, firsthealth event data associated with a first plurality of heath carerecords associated with a plurality of patients, said computer processorcontrolling a cloud hosted mediation system comprising an inferenceengine software application, a truth maintenance system database, andnon monotonic logic, wherein said non monotonic logic comprises code forenabling a Dempster Shafer theory; deriving, by said computer processorexecuting said inference engine software application, first healthrelated assumption data associated with each portion of portions of saidfirst health event data associated with associated patients of saidplurality of patients and related records in said truth maintenancesystem database, wherein said first health related assumption datacomprises multiple sets of assumptions associated with said plurality ofpatients, wherein each set of said multiple sets comprises assumedmedical conditions and an associated plausibility percentage value,wherein at least two sets of said multiple sets is associated with eachpatient of set plurality of patients, wherein a first set of saidmultiple sets comprises evidence supporting a first fact indicating thata first patient of said plurality of patients has a first medicalcondition of said assumed medical conditions with a first plausibilitypercentage value, wherein a second set of said multiple sets comprisesevidence supporting a second fact indicating that said first patient hasa second medical condition of said assumed medical conditions with asecond plausibility percentage value, wherein said first medicalcondition differs from said second medical condition, and wherein saidfirst plausibility percentage value differs from said secondplausibility percentage value; generating, by said computer processorbased on results of determining that said first set comprises a higherbelief assignment value than said second set, said deriving and saidfirst executing, an initial diagnosis and treatment recommendation forsaid first patient, said initial diagnosis and treatment recommendationassociated with said first set; retrieving, by said computer processorfrom said truth maintenance system database, previous health relatedassumption data derived from and associated with previous portions ofprevious health event data retrieved from said plurality of datasources, said previous health related assumption data derived at a timediffering from a time of said deriving, said previous health relatedevent data associated with previous health related events occurring at adifferent time from said first health event data; additionallyexecuting, by said computer processor executing said non monotoniclogic, the Dempster Shafer theory with respect to said first set, saidsecond set, said first patient, and said previous health relatedassumption data; modifying, by said computer processor based on resultsof said additionally executing, said first plausibility percentage valueof said first set and said second plausibility percentage value of saidsecond set; generating, by said computer processor based on said resultsof said additionally executing and said modifying, an updated diagnosisand treatment recommendation for said first patient; and generating, bysaid computer processor executing said non monotonic logic and saidinference engine software application, first updated health relatedassumption data associated with said first health related assumptiondata and said previous health related assumption data, wherein saidprevious health related assumption data, said first health relatedassumption data, and said first updated health related assumption dataeach comprise assumptions associated with detected medical conditions ofsaid plurality of patients.
 2. The method of claim 1, furthercomprising: executing, by said computer processor based on said firstupdated health related assumption data, health related actionsassociated with said plurality of patients.
 3. The method of claim 2,wherein said health related actions comprise treatment options for saidplurality of patients.
 4. The method of claim 3, wherein said healthrelated actions comprises implementing a pay by usage cloud meteringmodel associated with said plurality of patients.
 5. The method of claim2, further comprising: transmitting, by said computer processor to aplurality of health care providers, data describing said health relatedactions associated with said plurality of patients.
 6. The method ofclaim 1, wherein said previous health related assumption data, saidfirst health related assumption data, and said first updated healthrelated assumption data each comprise assumptions associated with saidplurality of patients.
 7. The method of claim 1, wherein said generatingfirst updated health related assumption data comprises retractingportions of said first health related assumption data and said previoushealth related assumption data.
 8. The method of claim 7, furthercomprising: retrieving, by said computer processor, historical patientdata and treatment options data; after said retracting, associating bysaid computer processor, said first updated health related assumptiondata with said historical patient data and said treatment options data;generating, by said computer processor, health related recommendationsassociated with said plurality of patients; and presenting, by saidcomputer processor via a dashboard view on a display device, said healthrelated recommendations.
 9. The method of claim 8, wherein said healthrelated recommendations comprise treatment options for said plurality ofpatients.
 10. The method of claim 1, further comprising: providing atleast one support service for at least one of creating, integrating,hosting, maintaining, and deploying computer-readable code in saidcomputing system, wherein the code in combination with the computingsystem is capable of performing the method of claim
 1. 11. A computerprogram product, comprising a non-transistory computer readable memorydevice storing a computer readable program code, said computer readableprogram code comprising an algorithm that when executed by a computerprocessor implements a method within a computing device, said methodcomprising: receiving, by said computer processor from a plurality ofdata sources, first health event data associated with a first pluralityof heath care records associated with a plurality of patients, saidcomputer processor controlling a cloud hosted mediation systemcomprising an inference engine software application, a truth maintenancesystem database, and non monotonic logic, wherein said non monotoniclogic comprises code for enabling a Dempster Shafer theory; deriving, bysaid computer processor executing said inference engine softwareapplication, first health related assumption data associated with eachportion of portions of said first health event data associated withassociated patients of said plurality of patients and related records insaid truth maintenance system database, wherein said first healthrelated assumption data comprises multiple sets of assumptionsassociated with said plurality of patients, wherein each set of saidmultiple sets comprises assumed medical conditions and an associatedplausibility percentage value, wherein at least two sets of saidmultiple sets is associated with each patient of set plurality ofpatients, wherein a first set of said multiple sets comprises evidencesupporting a first fact indicating that a first patient of saidplurality of patients has a first medical condition of said assumedmedical conditions with a first plausibility percentage value, wherein asecond set of said multiple sets comprises evidence supporting a secondfact indicating that said first patient has a second medical conditionof said assumed medical conditions with a second plausibility percentagevalue, wherein said first medical condition differs from said secondmedical condition, and wherein said first plausibility percentage valuediffers from said second plausibility percentage value; generating, bysaid computer processor based on results of determining that said firstset comprises a higher belief assignment value than said second set,said deriving and said first executing, an initial diagnosis andtreatment recommendation for said first patient, said initial diagnosisand treatment recommendation associated with said first set; retrieving,by said computer processor from said truth maintenance system database,previous health related assumption data derived from and associated withprevious portions of previous health event data retrieved from saidplurality of data sources, said previous health related assumption dataderived at a time differing from a time of said deriving, said previoushealth related event data associated with previous health related eventsoccurring at a different time from said first health event data;additionally executing, by said computer processor executing said nonmonotonic logic, the Dempster Shafer theory with respect to said firstset, said second set, said first patient, and said previous healthrelated assumption data; modifying, by said computer processor based onresults of said additionally executing, said first plausibilitypercentage value of said first set and said second plausibilitypercentage value of said second set; generating, by said computerprocessor based on said results of said additionally executing and saidmodifying, an updated diagnosis and treatment recommendation for saidfirst patient; and generating, by said computer processor executing saidnon monotonic logic and said inference engine software application,first updated health related assumption data associated with said firsthealth related assumption data and said previous health relatedassumption data, wherein said previous health related assumption data,said first health related assumption data, and said first updated healthrelated assumption data each comprise assumptions associated withdetected medical conditions of said plurality of patients.
 12. Thecomputer program product of claim 11, wherein said method furthercomprises: executing, by said computer processor based on said firstupdated health related assumption data, health related actionsassociated with said plurality of patients.
 13. The computer programproduct of claim 12, wherein said health related actions comprisetreatment options for said plurality of patients.
 14. The computerprogram product of claim 13, wherein said health related actionscomprises implementing a pay by usage cloud metering model associatedwith said plurality of patients.
 15. The computer program product ofclaim 12, wherein said method further comprises: transmitting, by saidcomputer processor to a plurality of health care providers, datadescribing said health related actions associated with said plurality ofpatients.
 16. The computer program product of claim 11, wherein saidprevious health related assumption data, said first health relatedassumption data, and said first updated health related assumption dataeach comprise assumptions associated with said plurality of patients.17. The computer program product of claim 11, wherein said generatingfirst updated health related assumption data comprises retractingportions of said first health related assumption data and said previoushealth related assumption data.
 18. The computer program product ofclaim 17, wherein said method further comprises: retrieving, by saidcomputer processor, historical patient data and treatment options data;after said retracting, associating by said computer processor, saidfirst updated health related assumption data with said historicalpatient data and said treatment options data; generating, by saidcomputer processor, health related recommendations associated with saidplurality of patients; and presenting, by said computer processor via adashboard view on a display device, said health related recommendations.19. A computing system comprising a computer processor coupled to acomputer-readable memory unit, said memory unit comprising instructionsthat when executed by the computer processor implements a methodcomprising: receiving, by said computer processor from a plurality ofdata sources, first health event data associated with a first pluralityof heath care records associated with a plurality of patients, saidcomputer processor controlling a cloud hosted mediation systemcomprising an inference engine software application, a truth maintenancesystem database, and non monotonic logic, wherein said non monotoniclogic comprises code for enabling a Dempster Shafer theory; deriving, bysaid computer processor executing said inference engine softwareapplication, first health related assumption data associated with eachportion of portions of said first health event data associated withassociated patients of said plurality of patients and related records insaid truth maintenance system database, wherein said first healthrelated assumption data comprises multiple sets of assumptionsassociated with said plurality of patients, wherein each set of saidmultiple sets comprises assumed medical conditions and an associatedplausibility percentage value, wherein at least two sets of saidmultiple sets is associated with each patient of set plurality ofpatients, wherein a first set of said multiple sets comprises evidencesupporting a first fact indicating that a first patient of saidplurality of patients has a first medical condition of said assumedmedical conditions with a first plausibility percentage value, wherein asecond set of said multiple sets comprises evidence supporting a secondfact indicating that said first patient has a second medical conditionof said assumed medical conditions with a second plausibility percentagevalue, wherein said first medical condition differs from said secondmedical condition, and wherein said first plausibility percentage valuediffers from said second plausibility percentage value; generating, bysaid computer processor based on results of determining that said firstset comprises a higher belief assignment value than said second set,said deriving and said first executing, an initial diagnosis andtreatment recommendation for said first patient, said initial diagnosisand treatment recommendation associated with said first set; retrieving,by said computer processor from said truth maintenance system database,previous health related assumption data derived from and associated withprevious portions of previous health event data retrieved from saidplurality of data sources, said previous health related assumption dataderived at a time differing from a time of said deriving, said previoushealth related event data associated with previous health related eventsoccurring at a different time from said first health event data;additionally executing, by said computer processor executing said nonmonotonic logic, the Dempster Shafer theory with respect to said firstset, said second set, said first patient, and said previous healthrelated assumption data; modifying, by said computer processor based onresults of said additionally executing, said first plausibilitypercentage value of said first set and said second plausibilitypercentage value of said second set; generating, by said computerprocessor based on said results of said additionally executing and saidmodifying, an updated diagnosis and treatment recommendation for saidfirst patient; and generating, by said computer processor executing saidnon monotonic logic and said inference engine software application,first updated health related assumption data associated with said firsthealth related assumption data and said previous health relatedassumption data, wherein said previous health related assumption data,said first health related assumption data, and said first updated healthrelated assumption data each comprise assumptions associated withdetected medical conditions of said plurality of patients.